{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T02:19:57Z","timestamp":1773541197003,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T00:00:00Z","timestamp":1602460800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T00:00:00Z","timestamp":1602460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2020,12]]},"DOI":"10.1007\/s11063-020-10357-x","type":"journal-article","created":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T03:10:33Z","timestamp":1602472233000},"page":"2469-2491","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Extracting Classification Rules from Artificial Neural Network Trained with Discretized Inputs"],"prefix":"10.1007","volume":"52","author":[{"given":"Dounia","family":"Yedjour","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,12]]},"reference":[{"key":"10357_CR1","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.engappai.2016.01.004","volume":"51","author":"A Boutorh","year":"2016","unstructured":"Boutorh A, Guessoum A (2016) Complex diseases SNP selection and classification by hybrid association rule mining and artificial neural network based evolutionary algorithms. Eng Appl Artif Intell 51:58\u201370","journal-title":"Eng Appl Artif Intell"},{"key":"10357_CR2","doi-asserted-by":"publisher","unstructured":"Fu K, Cheng DW, Tu Y, Zhang L (2016) Credit card fraud detection using convolutional neural networks. In: Proceedings of 23rd international conference on neural information processing, pp 483\u2013490. https:\/\/doi.org\/10.1007\/978-3-319-46675-0_53","DOI":"10.1007\/978-3-319-46675-0_53"},{"key":"10357_CR3","unstructured":"Ruz GA, Est\u00e9vez PA (2005) Image segmentation using fuzzy min-max neural networks for wood defect detection. In: Pham DT, Eldukhri EE, Soroka AJ (eds) Intelligent production machines and systems-first I*PROMS virtual conference, pp 183\u2013188"},{"key":"10357_CR4","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.rcim.2015.11.006","volume":"43","author":"H Fernando","year":"2017","unstructured":"Fernando H, Surgenor B (2017) An unsupervised artificial neural network versus a rule-based approach for fault detection and identification in an automated assembly machine. Robot Comput Integr Manuf 43:79\u201388","journal-title":"Robot Comput Integr Manuf"},{"key":"10357_CR5","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1016\/j.compbiomed.2006.10.005","volume":"37","author":"P Luukka","year":"2007","unstructured":"Luukka P (2007) Similarity classifier using similarity measure derived from Yu\u2019s norms in classification of medical datasets. Comput Biol Med 37:1133\u20131140","journal-title":"Comput Biol Med"},{"key":"10357_CR6","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1016\/j.neucom.2016.05.040","volume":"207","author":"Y Hayashi","year":"2016","unstructured":"Hayashi Y, Setiono R, Azcarraga A (2016) Neural network training and rule extraction with augmented discretized input. Neurocomputing 207:610\u2013622","journal-title":"Neurocomputing"},{"key":"10357_CR7","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.neucom.2012.01.024","volume":"86","author":"SH Yang","year":"2012","unstructured":"Yang SH, Chen YP (2012) An evolutionary constructive and pruning algorithm for artificial neural networks and its prediction applications. Neurocomputing 86:140\u2013149","journal-title":"Neurocomputing"},{"key":"10357_CR8","first-page":"71","volume":"131","author":"G Towell","year":"1993","unstructured":"Towell G, Shavlik JW (1993) The extraction of refined rules from knowledge-based neural networks. Mach Learn 131:71\u2013101","journal-title":"Mach Learn"},{"issue":"3","key":"10357_CR9","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1109\/69.774103","volume":"11","author":"I Taha","year":"1999","unstructured":"Taha I, Ghosh J (1999) Symbolic interpretation of artificial neural networks. IEEE Trans Knowl Data Eng 11(3):448\u2013463","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10357_CR10","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1016\/j.procs.2013.09.299","volume":"23","author":"I Khan","year":"2013","unstructured":"Khan I, Kulkarni A (2013) Knowledge extraction from survey data using neural networks. Proc Comput Sci 23:433\u2013438","journal-title":"Proc Comput Sci"},{"key":"10357_CR11","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.asoc.2018.08.007","volume":"72","author":"D Yedjour","year":"2018","unstructured":"Yedjour D, Benyettou A (2018) Symbolic interpretation of artificial neural networks based on multiobjective genetic algorithms and association rules mining. Appl Soft Comput 72:177\u2013188","journal-title":"Appl Soft Comput"},{"key":"10357_CR12","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/978-3-540-75396-4_7","volume":"82","author":"U Markowska-Kaczmar","year":"2008","unstructured":"Markowska-Kaczmar U (2008) Evolutionary approaches to rule extraction from neural networks. Stud Comput Intell (SCI) 82:177\u2013209","journal-title":"Stud Comput Intell (SCI)"},{"issue":"5","key":"10357_CR13","doi-asserted-by":"publisher","first-page":"2465","DOI":"10.3906\/elk-1801-75","volume":"26","author":"D Yedjour","year":"2018","unstructured":"Yedjour D, Aek Benyettou, Yedjour H (2018) Symbolic interpretation of artificial neural networks using genetic algorithms. Turk J Electr Eng Comput Sci 26(5):2465\u20132475. https:\/\/doi.org\/10.3906\/elk-1801-75","journal-title":"Turk J Electr Eng Comput Sci"},{"key":"10357_CR14","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1016\/j.neucom.2005.12.127","volume":"70","author":"ER Hruschka","year":"2006","unstructured":"Hruschka ER, Ebecken NFF (2006) Extracting rules from multilayer perceptrons in classification problems: a clustering-based approach. Neurocomputing 70:384\u2013397","journal-title":"Neurocomputing"},{"key":"10357_CR15","unstructured":"Craven M, Shavlik J (1996) Extracting tree-structured representations of trained networks. In: Touretzky DS, Mozer MC, Hasselmo M (eds) Advances in neural information processing systems, vol 8. MIT Press, pp 24\u201330"},{"key":"10357_CR16","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1016\/j.procs.2017.01.172","volume":"104","author":"A Bondarenko","year":"2017","unstructured":"Bondarenko A, Aleksejeva L, Jumutc V, Borisov A (2017) Classification tree extraction from trained artificial neural networks. Procedia Comput Sci 104:556\u2013563","journal-title":"Procedia Comput Sci"},{"key":"10357_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.engappai.2014.11.003","volume":"39","author":"F Ahmadizar","year":"2015","unstructured":"Ahmadizar F, Soltanian K, Akhlaghian F, Tsoulos I (2015) Artificial neural network development by means of a novel combination of grammatical evolution and genetic algorithm. Eng Appl Artif Intell 39:1\u201313","journal-title":"Eng Appl Artif Intell"},{"key":"10357_CR18","first-page":"327","volume-title":"Epistasis analysis using artificial intelligence, Epistasis","author":"JH Moore","year":"2015","unstructured":"Moore JH, Hill DP (2015) Epistasis analysis using artificial intelligence, Epistasis. Springer, New York, pp 327\u2013346"},{"key":"10357_CR19","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.neucom.2004.04.015","volume":"63","author":"U Markowska-Kaczmar","year":"2005","unstructured":"Markowska-Kaczmar U, Trelak W (2005) Fuzzy logic and evolutionary algorithm\u2014two techniques in rule extraction from neural networks. Neurocomputing 63:359\u2013379","journal-title":"Neurocomputing"},{"issue":"2","key":"10357_CR20","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s11063-011-9207-8","volume":"35","author":"M Augasta","year":"2012","unstructured":"Augasta M, Kathirvalavakumar T (2012) Reverse engineering the neural networks for rule extraction in classification problems. Neural Process Lett 35(2):131\u2013150","journal-title":"Neural Process Lett"},{"issue":"2","key":"10357_CR21","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1109\/TSMCC.2004.843220","volume":"36","author":"LB Gon\u00e7alves","year":"2006","unstructured":"Gon\u00e7alves LB, Bernardes MM, Vellasco R (2006) Inverted hierarchical neuro-fuzzy bsp system: a novel neuro-fuzzy model for pattern classification and rule extraction in databases. IEEE Trans Syst Man Cybern Part C Appl Rev 36(2):236\u2013248","journal-title":"IEEE Trans Syst Man Cybern Part C Appl Rev"},{"key":"10357_CR22","unstructured":"Liu H, Setiono R (1995) Chi2: feature selection and discretization of numeric attributes. In: Proceedings of Seventh Int\u2019l Conference on Tools with Artificial Intelligence, pp 388\u2013391"},{"key":"10357_CR23","unstructured":"Fu X, Wang L (2001) Rule extraction by genetic algorithms based on a simplified RBF neural network. In: Proceedings congress on evolutionary computation, pp 753\u2013758"},{"key":"10357_CR24","unstructured":"Markowska-Kaczmar U, Mularczyk K (2006) GA-based rule extraction\u00a0from\u00a0neural networks\u00a0for\u00a0approximation. In: Proceedings of the international multiconference on computer science and information technology, pp 141\u2013148"},{"key":"10357_CR25","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1016\/j.asoc.2015.10.032","volume":"40","author":"S Shinde","year":"2016","unstructured":"Shinde S, Kulkarni U (2016) Extracting classification rules from modified fuzzy min\u2013max neural network for data with mixed attributes. Appl Soft Comput 40:364\u2013378","journal-title":"Appl Soft Comput"},{"key":"10357_CR26","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.imu.2016.02.001","volume":"2","author":"Y Hayashi","year":"2016","unstructured":"Hayashi Y, Yukita S (2016) Rule extraction using recursive-rule extraction algorithm with J48graft combined with sampling selection techniques for the diagnosis of type 2 diabetes mellitus in the Pima Indian dataset. Inform Med Unlocked 2:92\u2013104","journal-title":"Inform Med Unlocked"},{"key":"10357_CR27","doi-asserted-by":"publisher","unstructured":"Zilke JR, Menc\u00eda EL, Janssen F (2016) Deepred\u2013rule extraction from deep neural networks. In: International conference on discovery science, Springer, pp 457\u2013473. https:\/\/doi.org\/10.1007\/978-3-319-46307-0_29","DOI":"10.1007\/978-3-319-46307-0_29"},{"key":"10357_CR28","doi-asserted-by":"publisher","unstructured":"Bologna G, Hayashi Y (2018) A comparison study on rule extraction from neural network ensembles, boosted shallow trees, and SVMs. In: Applied computational intelligence and soft computing. https:\/\/doi.org\/10.1155\/2018\/4084850","DOI":"10.1155\/2018\/4084850"},{"key":"10357_CR29","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/s00354-018-0048-0","volume":"37","author":"M Chakraborty","year":"2019","unstructured":"Chakraborty M, Biswas SK, Purkayastha B (2019) Rule extraction from neural network using input data ranges recursively. New Gener. Comput. 37:67\u201396. https:\/\/doi.org\/10.1007\/s00354-018-0048-0","journal-title":"New Gener. Comput."},{"key":"10357_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-04830-w","author":"S Mahdavifar","year":"2020","unstructured":"Mahdavifar S, Ghorbani AA (2020) DeNNeS: deep embedded neural network expert system for detecting cyber attacks. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-020-04830-w","journal-title":"Neural Comput Appl"},{"issue":"4","key":"10357_CR31","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/4235.797969","volume":"3","author":"E Zitzler","year":"1999","unstructured":"Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and strength Pareto approach. IEEE Trans Evolut Comput 3(4):257\u2013271","journal-title":"IEEE Trans Evolut Comput"},{"key":"10357_CR32","doi-asserted-by":"crossref","unstructured":"Elarbi M, Bechikh S, Ben Said L, Datta R (2017) Multi-objective optimization: classical and evolutionary approaches. In: Recent advances in evolutionary multi-objective optimization. Springer, Berlin, pp 1\u201330","DOI":"10.1007\/978-3-319-42978-6_1"},{"key":"10357_CR33","doi-asserted-by":"crossref","unstructured":"Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of ACM-SIMOD international conference on management of data, Washington, DC, pp 207\u2013216","DOI":"10.1145\/170036.170072"},{"key":"10357_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-26535-3_16","author":"MMJ Kabir","year":"2015","unstructured":"Kabir MMJ, Xu S, Kang BH, Zhao Z (2015) A new evolutionary algorithm for extracting a reduced set of interesting association rules. Neural Inf Process. https:\/\/doi.org\/10.1007\/978-3-319-26535-3_16","journal-title":"Neural Inf Process"},{"issue":"2","key":"10357_CR35","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/s10115-012-0591-9","volume":"38","author":"JM Luna","year":"2014","unstructured":"Luna JM, Romero JR, Ventura S (2014) On the adaptability of G3PARM to the extraction of rare association rules. Knowl Inf Syst 38(2):391\u2013418","journal-title":"Knowl Inf Syst"},{"key":"10357_CR36","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.artmed.2009.05.003","volume":"47","author":"I Gadaras","year":"2009","unstructured":"Gadaras I, Mikhailov L (2009) An interpretable fuzzy rule-based classification methodology for medical diagnosis. Artif Intell Med 47:25\u201341","journal-title":"Artif Intell Med"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-020-10357-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-020-10357-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-020-10357-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T23:07:01Z","timestamp":1669158421000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-020-10357-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,12]]},"references-count":36,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["10357"],"URL":"https:\/\/doi.org\/10.1007\/s11063-020-10357-x","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,12]]},"assertion":[{"value":"21 September 2020","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 October 2020","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}